1. Field of the Invention
The present invention relates to the field of computer science and, more particularly to a framework for restricting resources consumed by ghost agents disposed within a grid environment.
2. Description of the Related Art
A grid environment is a distributed computing environment where computing, application, storage, and/or network resources can be shared across geographically disperse organizations. An ideal grid environment allows flexible, secure, coordinated resource sharing among dynamic collections of individuals, organizations, and resources. In the grid environment, a variety of computing resources that contribute to a virtual resource pool can be transparently utilized on an as-needed basis. Grid computing resources in the virtual resource pool can be treated as commodities or services, which can be consumed in a manner similar to the commercial consumption of electricity and water.
While grid computing may presently be at an early stage in its evolution, several grid environments have been successfully implemented. One noteworthy implementation is the NC BioGrid Project that was successfully implemented in the fall of 2001 to enable researchers and educators throughout North Carolina to pool computing resources for use in sequencing genes and related genetic research. Other notable grid implementations include SETI@home, the Drug Design and Optimization Lab (D2OL), and EUROGRID. Additionally, commercially available software products exist for establishing a customizable grid environment, such as Avaki's data grid from Avaki of Burlington, Me. and Grid MP Enterprise from United Devices of Austin, Tex. Further, a number of readily available toolkits and standards have been developed for creating a grid environment including, for example, the Globus Toolkit provided by the Globus project and the Open Grid Services Architecture (OGSA).
A grid environment can include multiple applications. Each application can include a set of computing resources that performs a series of related tasks. Examples of applications include, but are not limited to, word processors, database programs, Web browsers, development tools, drawing applications, image editing programs, and communication programs. The various computing resources for one application can be distributed across several different grids within a grid environment, wherein each grid can contain a myriad of diverse hardware components, such as communication lines, networking routers, servers, workstations, peripherals, intranets, and the like.
The interdependencies that exist among applications and hardware resources in a grid environment can make it difficult for providers of grid-based applications to perform testing, installation, monitoring, and maintenance tasks. One such difficulty relates to induced environmental interferences. That is, methods for performing the above listed tasks can consume significant computing resources of the grid environment, which can be referred to as overhead. The overhead can affect operational characteristics of the grid environment. Consequently, results from the listed tasks can be compromised by the overhead resulting from task execution. If the amount of resources used in performing the listed tasks can be constrained, however, the disruptions induced by the tasks can be minimized.
Conventional methods for controlling the privileges granted to system resources include such methodologies as Java's sand box model by Sun Microsystems of Palo Alto, Calif., Free BSD's “jail” function by the FreeBSD Foundation, and Linux's chroot “jail” function. None of these methodologies, however, can provide resource consumption controls flexible enough to be generically utilized within a grid environment. Consequently, a new methodology is required that reduces undesirable perturbations resulting from overhead.